In 'Data Science for Marketing Analytics', you'll embark on a journey that integrates the power of data analytics with strategic marketing. With a focus on practical application, this guide walks you through using Python to analyze datasets, implement machine learning models, and derive data-driven insights. What this Book will help me do Gain expertise in cleaning, exploring, and visualizing marketing data using Python. Build machine learning models to predict customer behavior and sales outcomes. Leverage unsupervised learning techniques for effective customer segmentation. Compare and optimize predictive models using advanced evaluation methods. Master Python libraries like pandas and Matplotlib for data manipulation and visualization. Author(s) Mirza Rahim Baig, Gururajan Govindan, and Vishwesh Ravi Shrimali combine their extensive expertise in data analytics and marketing to bring you this comprehensive guide. Drawing from years of applying analytics in real-world marketing scenarios, they provide a hands-on approach to learning data science tools and techniques. Who is it for? This book is perfect for marketing professionals and analysts eager to harness the capabilities of Python to enhance their data-driven strategies. It is also ideal for data scientists looking to apply their skills in marketing across various roles. While a basic understanding of data analysis and Python will help, all key concepts are introduced comprehensively for beginners.

Mark and Ryan welcome back Adam Kamins, Director of Regional Economics at Moody's Analytics, to discuss the August job numbers, the Delta variant, and economic costs of Hurricane Ida. Full episode transcript can be found here.

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

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Summary The Presto project has become the de facto option for building scalable open source analytics in SQL for the data lake. In recent months the community has focused their efforts on making it the fastest possible option for running your analytics in the cloud. In this episode Dipti Borkar discusses the work that she and her team are doing at Ahana to simplify the work of running your own PrestoDB environment in the cloud. She explains how they are optimizin the runtime to reduce latency and increase query throughput, the ways that they are contributing back to the open source community, and the exciting improvements that are in the works to make Presto an even more powerful option for all of your analytics.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Schema changes, missing data, and volume anomalies caused by your data sources can happen without any advanced notice if you lack visibility into your data-in-motion. That leaves DataOps reactive to data quality issues and can make your consumers lose confidence in your data. By connecting to your pipeline orchestrator like Apache Airflow and centralizing your end-to-end metadata, Databand.ai lets you identify data quality issues and their root causes from a single dashboard. With Databand.ai, you’ll know whether the data moving from your sources to your warehouse will be available, accurate, and usable when it arrives. Go to dataengineeringpodcast.com/databand to sign up for a free 30-day trial of Databand.ai and take control of your data quality today. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Dipti Borkar, cofounder Ahana about Presto and Ahana, SaaS managed service for Presto

Interview

Introduction How did you get involved in the area of data management? Can you describe what Ahana is and the story behind it? There has been a lot of recent activity in the Presto community. Can you give an overview of the options that are available for someone wanting to use its SQL engine for querying their data?

What is Ahana’s role in the community/ecosystem? (happy to skip this question if it’s too contentious) What are some of the notable differences that have emerged over the past couple of years between the Trino (formerly PrestoSQL) and PrestoDB projects?

Another area that has been seeing a lot of activity is data lakes and projects to make them more manageable and feature complete (e.g. Hudi, Delta Lake, Iceberg, Nessie, LakeFS, etc.). How has that influenced your product focus and capabilities?

How does this activity change the calculus for organizations who are deciding on a lake or warehouse for their data architecture?

Can y

By reading "Data Analytics Made Easy," you'll gain a solid understanding of data analysis and visualization without requiring coding skills. This book emphasizes practical knowledge and use cases, covering storytelling, automation, machine learning, and business dashboards with tools like KNIME and Power BI. What this Book will help me do Understand the fundamentals of data analytics and how to leverage data for business insights. Create and automate data workflows using the no-code KNIME Analytics Platform. Develop interactive dashboards and data visualizations with Microsoft Power BI. Learn the basics of machine learning and how to apply models for business use. Enhance presentations and influence decisions through effective data storytelling. Author(s) None De Mauro is an experienced author and professional in the field of data analytics. Passionate about making complex topics approachable, None specializes in explaining technical concepts in simpler terms, ensuring readers can easily grasp and apply them in their work. Who is it for? This book is perfect for professionals or beginners who want to work with and interpret data effectively. Ideal for individuals in business roles or management positions looking to enhance their skills in data analytics and build a foundational understanding of machine learning and visualization.

Bill Gale, Senior Fellow at Brookings Institute, joins Mark and Ryan to discuss Fed Chair Jerome Powell's speech and the big topic was fiscal policy.

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

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This IBM® Redpaper publication provides guidance on building an enterprise-grade data lake by using IBM Spectrum® Scale and Cloudera Data Platform (CDP) Private Cloud Base for performing in-place Cloudera Hadoop or Cloudera Spark-based analytics. It also covers the benefits of the integrated solution and gives guidance about the types of deployment models and considerations during the implementation of these models. August 2021 update added CES protocol support in Hadoop environment

Elevate your reports with more user control and interactive elements Want to create exciting, user-friendly visualizations to bring greater intelligence to your organization? By mastering the full power of SAS Visual Analytics, you can add features that will enhance your reports and bring more depth and insight to your data. Interactive Reports in SAS Visual Analytics: Advanced Features and Customization is for experienced users who want to harness the advanced functionality of Visual Analytics on SAS Viya to create visualizations or augment existing reports. The book is full of real-world examples and step-by-step instructions to help you unlock the full potential of your reports. In this book, you will learn how to create interactive URL links to external websites use parameters to give the viewer more control add custom graphs and maps execute SAS code using SAS Viya jobs and more!

Erik Bernhardsson spent six years at Spotify, where he contributed to the first version of the music recommendation system. After a stint as CTO at Better.com, he's now working on building new infrastructure tooling for data teams. In this wide-ranging conversation with Tristan & Julia, Erik dives into the nuts and bolts of Spotify's recommendation algorithm, (paradoxically) why you should rarely need to use ML, and the fundamental infrastructure challenges that drag down the productivity of data teams. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Single-purpose databases were designed to address specific problems and use cases. Given this narrow focus, there are inherent tradeoffs required when trying to accommodate multiple datatypes or workloads in your enterprise environment. The result is data fragmentation that spills over into application development, IT operations, data security, system scalability, and availability. In this report, author Alice LaPlante explains why developing modern, data-driven applications may be easier and more synergistic when using a converged database. Senior developers, architects, and technical decision-makers will learn cloud-native application development techniques for working with both structured and unstructured data. You'll discover ways to run transactional and analytical workloads on a single, unified data platform. This report covers: Benefits and challenges of using a converged database to develop data-driven applications How to use one platform to work with both structured and unstructured data that includes JSON, XML, text and files, spatial and graph, Blockchain, IoT, time series, and relational data Modern development practices on a converged database, including API-driven development, containers, microservices, and event streaming Use case examples including online food delivery, real-time fraud detection, and marketing based on real-time analytics and geospatial targeting

If you want to increase Tableau's value to your organization, this practical book has your back. Authors Ann Jackson and Luke Stanke guide data analysts through strategies for solving real-world analytics problems using Tableau. Starting with the basics and building toward advanced topics such as multidimensional analysis and user experience, you'll explore pragmatic and creative examples that you can apply to your own data. Staying competitive today requires the ability to quickly analyze and visualize data and make data-driven decisions. With this guide, data practitioners and leaders alike will learn strategies for building compelling and purposeful visualizations, dashboards, and data products. Every chapter contains the why behind the solution and the technical knowledge you need to make it work. Use this book as a high-value on-the-job reference guide to Tableau Visualize different data types and tackle specific data challenges Create compelling data visualizations, dashboards, and data products Learn how to generate industry-specific analytics Explore categorical and quantitative analysis and comparisons Understand geospatial, dynamic, statistical, and multivariate analysis Communicate the value of the Tableau platform to your team and to stakeholders

In this episode of DataFramed, we speak with Brent Dykes, Senior Director of Insights & Data Storytelling at Blast Analytics and author of Effective Data Storytelling: How to Turn Insights into Action on how data storytelling is shaping the analytics space. 

Throughout the episode, Brent talks about his background, what made him write a book on effective data storytelling, how data storytelling is often misinterpreted and misused, the psychology of storytelling and how humans are shaped to resonate with it, the role of empathy when creating data stories, the blueprint of a successful data story, what data scientists can do to become better data storytellers, the future of augmented analytics and data storytelling, and more. 

Relevant links from the interview:

Connect with Brent on LinkedInRegister for Brent's Webinar on DataCampCheck out Brent's Book

Mark, Ryan, and Cris welcome back Marisa Di Natale, Senior Director at Moody's Analytics to discuss the impact of the Delta variant of COVID-19 on the U.S. economy. The big topic is the health of the American household balance sheet. Full episode transcript can be found here: https://about.moodys.io/podcast-episodes/delta-and-debt

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

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Public Policy Analytics teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. This book will appeal to researchers and data scientists at the intersection of data analytics and public policy, and readers who wish to understand how algorithms will affect the future of government.

Build a data platform to the industry-leading standards set by Microsoft’s own infrastructure. In Data Engineering on Azure you will learn how to: Pick the right Azure services for different data scenarios Manage data inventory Implement production quality data modeling, analytics, and machine learning workloads Handle data governance Using DevOps to increase reliability Ingesting, storing, and distributing data Apply best practices for compliance and access control Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft’s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning. About the Technology Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify. About the Book In Data Engineering on Azure you’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms. What's Inside Data inventory and data governance Assure data quality, compliance, and distribution Build automated pipelines to increase reliability Ingest, store, and distribute data Production-quality data modeling, analytics, and machine learning About the Reader For data engineers familiar with cloud computing and DevOps. About the Author Vlad Riscutia is a software architect at Microsoft. Quotes A definitive and complete guide on data engineering, with clear and easy-to-reproduce examples. - Kelum Prabath Senanayake, Echoworx An all-in-one Azure book, covering all a solutions architect or engineer needs to think about. - Albert Nogués, Danone A meaningful journey through the Azure ecosystem. You’ll be building pipelines and joining components quickly! - Todd Cook, Appen A gateway into the world of Azure for machine learning and DevOps engineers. - Krzysztof Kamyczek, Luxoft

Ways to learn more from Lillian: 

Data Science for Dummies Launch Party: Data Science For Dummies, 3rd Edition hits the streets in September, 2021 – but not without a proper launch party to celebrate. You’re invited! RSVP here: https://businessgrowth.ai/ The Data Entrepreneur’s Toolkit: A recommendation set for 32 free (or low-cost) tools & processes that'll actually grow your data business (even if you still haven’t put up that website yet!). https://www.data-mania.com/data-entrepreneur-toolkit/ The Data Superhero Quiz: A fun, free 45-second quiz that uncovers the ideal data career path for your personality type and skill set.https://data-mania.com/data-superhero-quiz Weekly Free Trainings: We currently publish 2 free trainings per week on YouTube! https://www.youtube.com/channel/UCK4MGP0A6lBjnQWAmcWBcKQ

Want to break into data science? Check out my new course coming out on August 18th: Data Career Jumpstart - https://www.datacareerjumpstart.com

Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

Daniel Rosen, founder of Rhodium Group, joins Mark, Ryan, and Cris to discuss all things China. Full episode transcript can be found here. Recommended Reads  Deng Xiaoping and the Transformation of China, by Ezra F. Vogel,  https://www.amazon.com/Deng-Xiaoping-Transformation-China-Vogel/dp/0674725867. Japan and China, Facing History, by Ezra F. Vogel,  https://www.amazon.com/s?k=china+and+japan+facing+history&i=stripbooks&crid=BEU5K0MDV8FW&sprefix=china+and+japan%2Cstripbooks%2C178&ref=nb_sb_ss_ts-doa-p_2_15

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

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Conheça as vagas do Grupo Boticário: https://bit.ly/GrupoBoticarioVagas

Como que o maior grupo de beleza do mundo utiliza dados no dia dia? Já imaginou o desafio de democratizar dados para milhares de pessoas de um grupo que existe a décadas? E como que AI e Machine Learning é aplicado na indústria da beleza? Isso e muito mais a gente conversa nesse papo sensacional com o pessoal do Grupo Boticário. Mariana Oliveira (Data Product Owner), Erika Cipriano (Analista de BI e Analytics) e Antonio Junior (Data Scientist) compartilham com a gente um pouco do dia a dia deles nesse papo bem descontraído.

Acesse nosso post do Medium pra ter acesso as referências do episódio: https://medium.com/data-hackers/trabalhando-com-dados-no-grupo-botic%C3%A1rio-data-hackers-podcast-43-9279a6e73815

In this episode, we're going to do something a little different, and turn the spotlight on co-host Julia Schottenstein. In this conversation with Tristan, you'll get to know Julia a bit—from her early childhood ambitions of becoming a "computer tycoon" (adorable!), to working in venture at NEA and now as a Product Manager at dbt Labs. They also dive into Julia's opinions on key trends shaping the future of the data industry (the phrase oligopoly makes an appearance). For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Digital transformation 2.0 is upon us! We have spent the last two decades digitising many products, services and processes to create digital experiences that are consistent, reliable and always on. That’s digital transformation 1.0 stuff. The next decade will be all about creating data-driven personalisation at scale. Rather than treating everyone the same in our digital environment, we will increasingly be using customer data to tailor the customer experience to individual customer needs. In this episode of Leaders of Analytics, we hear from Prashant Natarajan, Vice President of Strategy & Products at H2O.ai. Prashant has spent more than 15 years helping organisations to successful digital transformations through his leadership roles in the sphere of technology and AI. He has made it his career to demystify AI and digital transformation for organisations and their staff across multiple industries and continents. In this episode of Leaders of Analytics, we discuss: what’s required to do digital transformation 2.0 successfullyhow to create data-first organisationshow to use AI to take the robot out of humansthe future of automated machine learninghow organisations can ensure that their data science investments deliver actual business outcomesour upcoming book, Demystifying AI for the Enterprise, which Prashant and I have co-authored alongside 5 other domain experts.

Have you ever thought, "you know, it would be interesting to take my analytical knowledge and just totally run an organization based on what the data says?" Yeah. Us, either. That's terrifying! But, that's exactly what our guest on this episode did. Ben Lindbergh, along with his stathead-in-crime (aka, co-author) Sam Miller, took over the management of a minor league baseball team in 2015, and the result was The Only Rule Is It Has to Work: Our Wild Experiment Building a New Kind of Baseball Team. How does that apply to analytics in the business world? In a surprising number of ways, it turns out! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.